from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

# General docstring
_SCREAMING_SNAKE_CASE 		=      """RegNetConfig"""

# Base docstring
_SCREAMING_SNAKE_CASE 		=      """facebook/regnet-y-040"""
_SCREAMING_SNAKE_CASE 		=      [1, 1_0_8_8, 7, 7]

# Image classification docstring
_SCREAMING_SNAKE_CASE 		=      """facebook/regnet-y-040"""
_SCREAMING_SNAKE_CASE 		=      """tabby, tabby cat"""

_SCREAMING_SNAKE_CASE 		=      [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int = 3   ,   lowerCamelCase_						:						int = 1   ,   lowerCamelCase_						:						int = 1   ,   lowerCamelCase_						:						Optional[str] = "relu"   ,   **lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    # The padding and conv has been verified in
    # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
    UpperCamelCase			  =      tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2				)
    UpperCamelCase			  =      tf.keras.layers.ConvaD(
        filters=lowerCamelCase_   ,   kernel_size=lowerCamelCase_   ,   strides=lowerCamelCase_   ,   padding="""VALID"""   ,   groups=lowerCamelCase_   ,   use_bias=lowerCamelCase_   ,   name="""convolution"""   ,   )
    UpperCamelCase			  =      tf.keras.layers.BatchNormalization(epsilon=1E-5   ,   momentum=0.9   ,   name="""normalization"""				)
    UpperCamelCase			  =      ACTaFN[activation] if activation is not None else tf.identity







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.convolution(self.padding(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.normalization(lowerCamelCase_				)
    UpperCamelCase			  =      self.activation(lowerCamelCase_				)
    return hidden_state





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						RegNetConfig   ,   **lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      config.num_channels
    UpperCamelCase			  =      TFRegNetConvLayer(
        out_channels=config.embedding_size   ,   kernel_size=3   ,   stride=2   ,   activation=config.hidden_act   ,   name="""embedder"""   ,   )







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      shape_list(lowerCamelCase_				)[1]
    if tf.executing_eagerly() and num_channels != self.num_channels:
     raise ValueError(
         """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""				)

    # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
    # So change the input format from `NCHW` to `NHWC`.
    # shape = (batch_size, in_height, in_width, in_channels=num_channels)
    UpperCamelCase			  =      tf.transpose(lowerCamelCase_   ,   perm=(0, 2, 3, 1)				)
    UpperCamelCase			  =      self.embedder(lowerCamelCase_				)
    return hidden_state





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int = 2   ,   **lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      tf.keras.layers.ConvaD(
        filters=lowerCamelCase_   ,   kernel_size=1   ,   strides=lowerCamelCase_   ,   use_bias=lowerCamelCase_   ,   name="""convolution"""				)
    UpperCamelCase			  =      tf.keras.layers.BatchNormalization(epsilon=1E-5   ,   momentum=0.9   ,   name="""normalization"""				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						tf.Tensor   ,   lowerCamelCase_						:						bool = False				):





    """simple docstring"""


    return self.normalization(self.convolution(lowerCamelCase_				)   ,   training=lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int   ,   **lowerCamelCase_						:						Any				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase_   ,   name="""pooler"""				)
    UpperCamelCase			  =      [
        tf.keras.layers.ConvaD(filters=lowerCamelCase_   ,   kernel_size=1   ,   activation="""relu"""   ,   name="""attention.0"""				),
        tf.keras.layers.ConvaD(filters=lowerCamelCase_   ,   kernel_size=1   ,   activation="""sigmoid"""   ,   name="""attention.2"""				),
    ]







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.pooler(lowerCamelCase_				)
    for layer_module in self.attention:
     UpperCamelCase			  =      layer_module(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_state * pooled
    return hidden_state





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						Tuple   ,   lowerCamelCase_						:						RegNetConfig   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int = 1   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      in_channels != out_channels or stride != 1
    UpperCamelCase			  =      max(1   ,   out_channels // config.groups_width				)
    UpperCamelCase			  =      (
        TFRegNetShortCut(lowerCamelCase_   ,   stride=lowerCamelCase_   ,   name="""shortcut"""				)
        if should_apply_shortcut
        else tf.keras.layers.Activation("""linear"""   ,   name="""shortcut"""				)
    )
    # `self.layers` instead of `self.layer` because that is a reserved argument.
    UpperCamelCase			  =      [
        TFRegNetConvLayer(lowerCamelCase_   ,   kernel_size=1   ,   activation=config.hidden_act   ,   name="""layer.0"""				),
        TFRegNetConvLayer(
            lowerCamelCase_   ,   stride=lowerCamelCase_   ,   groups=lowerCamelCase_   ,   activation=config.hidden_act   ,   name="""layer.1"""				),
        TFRegNetConvLayer(lowerCamelCase_   ,   kernel_size=1   ,   activation=lowerCamelCase_   ,   name="""layer.2"""				),
    ]
    UpperCamelCase			  =      ACTaFN[config.hidden_act]







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_state
    for layer_module in self.layers:
     UpperCamelCase			  =      layer_module(lowerCamelCase_				)
    UpperCamelCase			  =      self.shortcut(lowerCamelCase_				)
    hidden_state += residual
    UpperCamelCase			  =      self.activation(lowerCamelCase_				)
    return hidden_state





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						RegNetConfig   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int = 1   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      in_channels != out_channels or stride != 1
    UpperCamelCase			  =      max(1   ,   out_channels // config.groups_width				)
    UpperCamelCase			  =      (
        TFRegNetShortCut(lowerCamelCase_   ,   stride=lowerCamelCase_   ,   name="""shortcut"""				)
        if should_apply_shortcut
        else tf.keras.layers.Activation("""linear"""   ,   name="""shortcut"""				)
    )
    UpperCamelCase			  =      [
        TFRegNetConvLayer(lowerCamelCase_   ,   kernel_size=1   ,   activation=config.hidden_act   ,   name="""layer.0"""				),
        TFRegNetConvLayer(
            lowerCamelCase_   ,   stride=lowerCamelCase_   ,   groups=lowerCamelCase_   ,   activation=config.hidden_act   ,   name="""layer.1"""				),
        TFRegNetSELayer(lowerCamelCase_   ,   reduced_channels=int(round(in_channels / 4				)				)   ,   name="""layer.2"""				),
        TFRegNetConvLayer(lowerCamelCase_   ,   kernel_size=1   ,   activation=lowerCamelCase_   ,   name="""layer.3"""				),
    ]
    UpperCamelCase			  =      ACTaFN[config.hidden_act]







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_state
    for layer_module in self.layers:
     UpperCamelCase			  =      layer_module(lowerCamelCase_				)
    UpperCamelCase			  =      self.shortcut(lowerCamelCase_				)
    hidden_state += residual
    UpperCamelCase			  =      self.activation(lowerCamelCase_				)
    return hidden_state





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						RegNetConfig   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int = 2   ,   lowerCamelCase_						:						int = 2   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)

    UpperCamelCase			  =      TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
    UpperCamelCase			  =      [
        # downsampling is done in the first layer with stride of 2
        layer(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   stride=lowerCamelCase_   ,   name="""layers.0"""				),
        *[layer(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   name=f"""layers.{i+1}"""				) for i in range(depth - 1				)],
    ]







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    for layer_module in self.layers:
     UpperCamelCase			  =      layer_module(lowerCamelCase_				)
    return hidden_state





class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						RegNetConfig   ,   **lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      []
    # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
    self.stages.append(
        TFRegNetStage(
            lowerCamelCase_   ,   config.embedding_size   ,   config.hidden_sizes[0]   ,   stride=2 if config.downsample_in_first_stage else 1   ,   depth=config.depths[0]   ,   name="""stages.0"""   ,   )				)
    UpperCamelCase			  =      zip(config.hidden_sizes   ,   config.hidden_sizes[1:]				)
    for i, ((in_channels, out_channels), depth) in enumerate(zip(lowerCamelCase_   ,   config.depths[1:]				)				):
     self.stages.append(TFRegNetStage(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   depth=lowerCamelCase_   ,   name=f"""stages.{i+1}"""				)				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						tf.Tensor   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    UpperCamelCase			  =      () if output_hidden_states else None

    for stage_module in self.stages:
     if output_hidden_states:
      UpperCamelCase			  =      hidden_states + (hidden_state,)

     UpperCamelCase			  =      stage_module(lowerCamelCase_				)

    if output_hidden_states:
     UpperCamelCase			  =      hidden_states + (hidden_state,)

    if not return_dict:
     return tuple(v for v in [hidden_state, hidden_states] if v is not None				)

    return TFBaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase_   ,   hidden_states=lowerCamelCase_				)





@keras_serializable
class SCREAMING_SNAKE_CASE_		(      tf.keras.layers.Layer		):
   __lowerCAmelCase           =				RegNetConfig







   def __init__(     self						:						str   ,   lowerCamelCase_						:						List[Any]   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      config
    UpperCamelCase			  =      TFRegNetEmbeddings(lowerCamelCase_   ,   name="""embedder"""				)
    UpperCamelCase			  =      TFRegNetEncoder(lowerCamelCase_   ,   name="""encoder"""				)
    UpperCamelCase			  =      tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase_   ,   name="""pooler"""				)







   @unpack_inputs
   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						tf.Tensor   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						bool = False   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCamelCase			  =      return_dict if return_dict is not None else self.config.use_return_dict

    UpperCamelCase			  =      self.embedder(lowerCamelCase_   ,   training=lowerCamelCase_				)

    UpperCamelCase			  =      self.encoder(
        lowerCamelCase_   ,   output_hidden_states=lowerCamelCase_   ,   return_dict=lowerCamelCase_   ,   training=lowerCamelCase_				)

    UpperCamelCase			  =      encoder_outputs[0]
    UpperCamelCase			  =      self.pooler(lowerCamelCase_				)

    # Change to NCHW output format have uniformity in the modules
    UpperCamelCase			  =      tf.transpose(lowerCamelCase_   ,   perm=(0, 3, 1, 2)				)
    UpperCamelCase			  =      tf.transpose(lowerCamelCase_   ,   perm=(0, 3, 1, 2)				)

    # Change the other hidden state outputs to NCHW as well
    if output_hidden_states:
     UpperCamelCase			  =      tuple([tf.transpose(lowerCamelCase_   ,   perm=(0, 3, 1, 2)				) for h in encoder_outputs[1]]				)

    if not return_dict:
     return (last_hidden_state, pooled_output) + encoder_outputs[1:]

    return TFBaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=lowerCamelCase_   ,   pooler_output=lowerCamelCase_   ,   hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states   ,   )





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				RegNetConfig
   __lowerCAmelCase           =				"""regnet"""
   __lowerCAmelCase           =				"""pixel_values"""







   @property
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)   ,   dtype=tf.floataa				)}





_SCREAMING_SNAKE_CASE 		=      r"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

_SCREAMING_SNAKE_CASE 		=      r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""  ,     __lowerCAmelCase  ,     )
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						RegNetConfig   ,   *lowerCamelCase_						:						List[str]   ,   **lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    super().__init__(lowerCamelCase_   ,   *lowerCamelCase_   ,   **lowerCamelCase_				)
    UpperCamelCase			  =      TFRegNetMainLayer(lowerCamelCase_   ,   name="""regnet"""				)







   @unpack_inputs
   @add_start_docstrings_to_model_forward(lowerCamelCase_				)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC   ,   output_type=lowerCamelCase_   ,   config_class=_CONFIG_FOR_DOC   ,   modality="""vision"""   ,   expected_output=_EXPECTED_OUTPUT_SHAPE   ,   )
   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						tf.Tensor   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						str=False   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCamelCase			  =      return_dict if return_dict is not None else self.config.use_return_dict

    UpperCamelCase			  =      self.regnet(
        pixel_values=lowerCamelCase_   ,   output_hidden_states=lowerCamelCase_   ,   return_dict=lowerCamelCase_   ,   training=lowerCamelCase_   ,   )
    if not return_dict:
     return (outputs[0],) + outputs[1:]

    return TFBaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=outputs.last_hidden_state   ,   pooler_output=outputs.pooler_output   ,   hidden_states=outputs.hidden_states   ,   )





@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """  ,     __lowerCAmelCase  ,     )
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						RegNetConfig   ,   *lowerCamelCase_						:						List[str]   ,   **lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__(lowerCamelCase_   ,   *lowerCamelCase_   ,   **lowerCamelCase_				)
    UpperCamelCase			  =      config.num_labels
    UpperCamelCase			  =      TFRegNetMainLayer(lowerCamelCase_   ,   name="""regnet"""				)
    # classification head
    UpperCamelCase			  =      [
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(config.num_labels   ,   name="""classifier.1"""				) if config.num_labels > 0 else tf.identity,
    ]







   @unpack_inputs
   @add_start_docstrings_to_model_forward(lowerCamelCase_				)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT   ,   output_type=lowerCamelCase_   ,   config_class=_CONFIG_FOR_DOC   ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,   )
   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						tf.Tensor = None   ,   lowerCamelCase_						:						tf.Tensor = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						str=False   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCamelCase			  =      return_dict if return_dict is not None else self.config.use_return_dict

    UpperCamelCase			  =      self.regnet(
        lowerCamelCase_   ,   output_hidden_states=lowerCamelCase_   ,   return_dict=lowerCamelCase_   ,   training=lowerCamelCase_				)

    UpperCamelCase			  =      outputs.pooler_output if return_dict else outputs[1]

    UpperCamelCase			  =      self.classifier[0](lowerCamelCase_				)
    UpperCamelCase			  =      self.classifier[1](lowerCamelCase_				)

    UpperCamelCase			  =      None if labels is None else self.hf_compute_loss(labels=lowerCamelCase_   ,   logits=lowerCamelCase_				)

    if not return_dict:
     UpperCamelCase			  =      (logits,) + outputs[2:]
     return ((loss,) + output) if loss is not None else output

    return TFSequenceClassifierOutput(loss=lowerCamelCase_   ,   logits=lowerCamelCase_   ,   hidden_states=outputs.hidden_states				)

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
